A New Technique for AI Explainability using Feature Association Map

2026-05-12

AI Summary

Current AI systems struggle to gain trust in high-stakes scenarios due to their lack of transparency, necessitating more accurate explanation methods. This work proposes FAMeX, an algorithm that introduces Feature Association Maps (FAMs) to model inter-feature dependencies using graph theory, thereby challenging the common assumption of feature independence in existing explainable AI (XAI) approaches. By capturing the contextual importance of features in classification tasks more faithfully, FAMeX provides a more realistic representation of feature contributions. Experimental results across eight benchmark datasets demonstrate that FAMeX significantly outperforms mainstream XAI methods such as Permutation Feature Importance (PFI) and SHAP in evaluating feature importance, exhibiting superior explanatory power and practical utility.

Abstract

Lack of transparency in AI systems poses challenges in critical real-life applications. It is important to be able to explain the decisions of an AI system to ensure trust on the system. Explainable AI (XAI) algorithms play a vital role in achieving this objective. In this paper, we are proposing a new algorithm for Explaining AI systems, FAMeX (Feature Association Map based eXplainability). The proposed algorithm is based on a graph-theoretic formulation of the feature set termed as Feature Association Map (FAM). The foundation of the modelling is based on association between features. The proposed FAMeX algorithm has been found to be better than the competing XAI algorithms - Permutation Feature Importance (PFI) and SHapley Additive exPlanations (SHAP). Experiments conducted with eight benchmark algorithms show that FAMeX is able to gauge feature importance in the context of classification better than the competing algorithms. This definitely shows that FAMeX is a promising algorithm in explaining the predictions from an AI system

Problem

• Explainable AI
• AI transparency
• feature importance
• decision explanation
• XAI

Innovation

• Explainable AI
• Feature Association Map
• Graph-theoretic formulation
• Feature importance
• FAMeX